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import os |
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import torch |
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import argparse |
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import numpy as np |
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from scipy.io.wavfile import write |
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import torchaudio |
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import utils |
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from Mels_preprocess import MelSpectrogramFixed |
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from torch.nn import functional as F |
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from hierspeechpp_speechsynthesizer import ( |
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SynthesizerTrn, Wav2vec2 |
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) |
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from ttv_v1.text import text_to_sequence |
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from ttv_v1.t2w2v_transformer import SynthesizerTrn as Text2W2V |
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from speechsr24k.speechsr import SynthesizerTrn as SpeechSR24 |
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from speechsr48k.speechsr import SynthesizerTrn as SpeechSR48 |
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from denoiser.generator import MPNet |
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from denoiser.infer import denoise |
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import amfm_decompy.basic_tools as basic |
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import amfm_decompy.pYAAPT as pYAAPT |
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seed = 1111 |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed(seed) |
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np.random.seed(seed) |
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def get_yaapt_f0(audio, rate=16000, interp=False): |
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frame_length = 20.0 |
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to_pad = int(frame_length / 1000 * rate) // 2 |
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f0s = [] |
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for y in audio.astype(np.float64): |
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y_pad = np.pad(y.squeeze(), (to_pad, to_pad), "constant", constant_values=0) |
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signal = basic.SignalObj(y_pad, rate) |
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pitch = pYAAPT.yaapt(signal, **{'frame_length': frame_length, 'frame_space': 5.0, 'nccf_thresh1': 0.25, |
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'tda_frame_length': 25.0, 'f0_max':1100}) |
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if interp: |
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f0s += [pitch.samp_interp[None, None, :]] |
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else: |
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f0s += [pitch.samp_values[None, None, :]] |
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f0 = np.vstack(f0s) |
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return f0 |
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def load_text(fp): |
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with open(fp, 'r') as f: |
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filelist = [line.strip() for line in f.readlines()] |
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return filelist |
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def load_checkpoint(filepath, device): |
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print(filepath) |
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assert os.path.isfile(filepath) |
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print("Loading '{}'".format(filepath)) |
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checkpoint_dict = torch.load(filepath, map_location=device) |
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print("Complete.") |
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return checkpoint_dict |
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def get_param_num(model): |
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num_param = sum(param.numel() for param in model.parameters()) |
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return num_param |
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def intersperse(lst, item): |
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result = [item] * (len(lst) * 2 + 1) |
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result[1::2] = lst |
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return result |
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def add_blank_token(text): |
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text_norm = intersperse(text, 0) |
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text_norm = torch.LongTensor(text_norm) |
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return text_norm |
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def VC(a, hierspeech): |
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net_g, speechsr, denoiser, mel_fn, w2v = hierspeech |
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os.makedirs(a.output_dir, exist_ok=True) |
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source_audio, sample_rate = torchaudio.load(a.source_speech) |
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if sample_rate != 16000: |
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source_audio = torchaudio.functional.resample(source_audio, sample_rate, 16000, resampling_method="kaiser_window") |
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p = (source_audio.shape[-1] // 1280 + 1) * 1280 - source_audio.shape[-1] |
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source_audio = torch.nn.functional.pad(source_audio, (0, p), mode='constant').data |
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file_name_s = os.path.splitext(os.path.basename(a.source_speech))[0] |
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try: |
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f0 = get_yaapt_f0(source_audio.numpy()) |
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except: |
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f0 = np.zeros((1, 1, source_audio.shape[-1] // 80)) |
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f0 = f0.astype(np.float32) |
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f0 = f0.squeeze(0) |
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ii = f0 != 0 |
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f0[ii] = (f0[ii] - f0[ii].mean()) / f0[ii].std() |
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y_pad = F.pad(source_audio, (40, 40), "reflect") |
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x_w2v = w2v(y_pad.cuda()) |
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x_length = torch.LongTensor([x_w2v.size(2)]).to(device) |
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target_audio, sample_rate = torchaudio.load(a.target_speech) |
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target_audio = target_audio[:1,:] |
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if sample_rate != 16000: |
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target_audio = torchaudio.functional.resample(target_audio, sample_rate, 16000, resampling_method="kaiser_window") |
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if a.scale_norm == 'prompt': |
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prompt_audio_max = torch.max(target_audio.abs()) |
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try: |
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t_f0 = get_yaapt_f0(target_audio.numpy()) |
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except: |
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t_f0 = np.zeros((1, 1, target_audio.shape[-1] // 80)) |
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t_f0 = t_f0.astype(np.float32) |
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t_f0 = t_f0.squeeze(0) |
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j = t_f0 != 0 |
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f0[ii] = ((f0[ii] * t_f0[j].std()) + t_f0[j].mean()).clip(min=0) |
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denorm_f0 = torch.log(torch.FloatTensor(f0+1).cuda()) |
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ori_prompt_len = target_audio.shape[-1] |
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p = (ori_prompt_len // 1600 + 1) * 1600 - ori_prompt_len |
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target_audio = torch.nn.functional.pad(target_audio, (0, p), mode='constant').data |
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file_name_t = os.path.splitext(os.path.basename(a.target_speech))[0] |
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if a.denoise_ratio == 0: |
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target_audio = torch.cat([target_audio.cuda(), target_audio.cuda()], dim=0) |
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else: |
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with torch.no_grad(): |
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denoised_audio = denoise(target_audio.squeeze(0).cuda(), denoiser, hps_denoiser) |
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target_audio = torch.cat([target_audio.cuda(), denoised_audio[:,:target_audio.shape[-1]]], dim=0) |
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target_audio = target_audio[:,:ori_prompt_len] |
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trg_mel = mel_fn(target_audio.cuda()) |
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trg_length = torch.LongTensor([trg_mel.size(2)]).to(device) |
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trg_length2 = torch.cat([trg_length,trg_length], dim=0) |
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with torch.no_grad(): |
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converted_audio = \ |
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net_g.voice_conversion_noise_control(x_w2v, x_length, trg_mel, trg_length2, denorm_f0, noise_scale=a.noise_scale_vc, denoise_ratio=a.denoise_ratio) |
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if a.output_sr == 48000 or 24000: |
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converted_audio = speechsr(converted_audio) |
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converted_audio = converted_audio.squeeze() |
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if a.scale_norm == 'prompt': |
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converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * prompt_audio_max |
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else: |
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converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * 0.999 |
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converted_audio = converted_audio.cpu().numpy().astype('int16') |
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file_name2 = "{}.wav".format(file_name_s+"_to_"+file_name_t) |
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output_file = os.path.join(a.output_dir, file_name2) |
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if a.output_sr == 48000: |
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write(output_file, 48000, converted_audio) |
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elif a.output_sr == 24000: |
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write(output_file, 24000, converted_audio) |
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else: |
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write(output_file, 16000, converted_audio) |
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def model_load(a): |
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mel_fn = MelSpectrogramFixed( |
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sample_rate=hps.data.sampling_rate, |
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n_fft=hps.data.filter_length, |
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win_length=hps.data.win_length, |
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hop_length=hps.data.hop_length, |
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f_min=hps.data.mel_fmin, |
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f_max=hps.data.mel_fmax, |
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n_mels=hps.data.n_mel_channels, |
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window_fn=torch.hann_window |
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).cuda() |
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w2v = Wav2vec2().cuda() |
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net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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**hps.model).cuda() |
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net_g.load_state_dict(torch.load(a.ckpt)) |
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_ = net_g.eval() |
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if a.output_sr == 48000: |
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speechsr = SpeechSR48(h_sr48.data.n_mel_channels, |
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h_sr48.train.segment_size // h_sr48.data.hop_length, |
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**h_sr48.model).cuda() |
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utils.load_checkpoint(a.ckpt_sr48, speechsr, None) |
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speechsr.eval() |
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elif a.output_sr == 24000: |
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speechsr = SpeechSR24(h_sr.data.n_mel_channels, |
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h_sr.train.segment_size // h_sr.data.hop_length, |
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**h_sr.model).cuda() |
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utils.load_checkpoint(a.ckpt_sr, speechsr, None) |
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speechsr.eval() |
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else: |
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speechsr = None |
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denoiser = MPNet(hps_denoiser).cuda() |
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state_dict = load_checkpoint(a.denoiser_ckpt, device) |
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denoiser.load_state_dict(state_dict['generator']) |
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denoiser.eval() |
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return net_g, speechsr, denoiser, mel_fn, w2v |
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def inference(a): |
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hierspeech = model_load(a) |
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VC(a, hierspeech) |
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def main(): |
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print('Initializing Inference Process..') |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--source_speech', default='example/reference_2.wav') |
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parser.add_argument('--target_speech', default='example/reference_1.wav') |
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parser.add_argument('--output_dir', default='output') |
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parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v2_ckpt.pth') |
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parser.add_argument('--ckpt_sr', type=str, default='./speechsr24k/G_340000.pth') |
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parser.add_argument('--ckpt_sr48', type=str, default='./speechsr48k/G_100000.pth') |
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parser.add_argument('--denoiser_ckpt', type=str, default='denoiser/g_best') |
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parser.add_argument('--scale_norm', type=str, default='max') |
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parser.add_argument('--output_sr', type=float, default=48000) |
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parser.add_argument('--noise_scale_ttv', type=float, |
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default=0.333) |
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parser.add_argument('--noise_scale_vc', type=float, |
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default=0.333) |
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parser.add_argument('--denoise_ratio', type=float, |
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default=0.8) |
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a = parser.parse_args() |
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global device, hps, h_sr,h_sr48, hps_denoiser |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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hps = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt)[0], 'config.json')) |
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h_sr = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr)[0], 'config.json') ) |
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h_sr48 = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr48)[0], 'config.json') ) |
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hps_denoiser = utils.get_hparams_from_file(os.path.join(os.path.split(a.denoiser_ckpt)[0], 'config.json')) |
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inference(a) |
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if __name__ == '__main__': |
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main() |